adaptive-accrual-failure-detector
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    1.0.0 • Public • Published

    adaptive-accrual-failure-detector

    npm version

    Failure detection for processes, connections and distributed systems. This is an implementation for JavaScript and TypeScript of a failure detector that uses an adaptive accrual algorithm. The theory of this detector is taken from the paper A New Adaptive Accrual Failure Detector for Dependable Distributed System authored by Benjamin Satzger, Andreas Pietzowski, Wolfang Trumler and Theo Ungerer.

    This detector is useful for detecting things such as network failures between two nodes.

    Usage

    The failure detector is based on incoming heartbeats and has a few options that can be used when creating the detector:

    • sampleSize - number of samples kept and used when calculating probability of a failure. A higher number of samples means the probability calculation is more stable but uses more memory. Default is 1000.
    • scalingFactor - factor used to scale failure probabilities, used to reduce overestimation of failure. Default is 0.9.
    • failureThreshold - the probability needed to to detect something as a failure. If the probability of failure is above this the thing being monitored is considered failed. Default is 0.5.
    // Using ES Module environment
    import { FailureDetector } from 'adaptive-accrual-failure-detector';
    
    const detector = new FailureDetector({
      failureThreshold: 0.6
    });

    When you receive a heartbeat you should call registerHeartbeat on the detector:

    detector.registerHeartbeat();

    To calculate if a failure has occurred you can call checkFailure() or calculateFailureProbability():

    // Check failure, will return true if failed
    const isFailed = detector.checkFailure();
    
    // Calculate the probability of failure between 0 and 1
    const failureProbability = detector.calculateFailureProbability();

    Install

    npm i adaptive-accrual-failure-detector

    DownloadsWeekly Downloads

    5

    Version

    1.0.0

    License

    MIT

    Unpacked Size

    23.2 kB

    Total Files

    13

    Last publish

    Collaborators

    • aholstenson